# Inside this Docker container we mount the directory with all the source data as "/projectdir/"
rootdir <- "/projectdir/"
source(paste0(rootdir, "/runStartup.R"))

barcodes <- read.delim(paste0(rootdir, "/source_data/GSE250629_scrnaseq_coldata_unfiltered.tsv.gz"), header = TRUE) %>%
  magrittr::set_rownames(paste(.$barcode, .$sample, sep = "_")) %>%
  dplyr::mutate(
    sample = factor(sample),
    genotype = factor(gsub("_.*", "", sample), levels = c("WT", "URE")),
    replicate = factor(gsub(".*_", "", sample))
  ) %>%
  dplyr::group_by(sample) %>%
  DataFrame() %>%
  `rownames<-`(.$cell)

features <- read.delim(paste0(rootdir, "/source_data/GSE250629_scrnaseq_rowdata_unfiltered.tsv.gz"), header = TRUE) %>%
  `rownames<-`(.$gene)

matrix <- Matrix::readMM(paste0(rootdir, "/source_data/GSE250629_scrnaseq_rawCounts_unfiltered.mtx.gz")) %>%
  as(., "CsparseMatrix")

rownames(matrix) <- rownames(features)
colnames(matrix) <- rownames(barcodes)

sce <- SingleCellExperiment(assays = list(counts = matrix), colData = barcodes, rowData = features)
rm(barcodes, features, matrix)

# Per-cell QC metrics
options(scipen = 999)
qcm <- scuttle::perCellQCMetrics(x = sce, subsets = list(rRNA = rowData(sce)$gene_type == "rRNA", mito = grepl("^mt-", rowData(sce)$gene_name)))
colData(sce) <- cbind(colData(sce), qcm)
rm(qcm)

# Calculate cutoffs based on MAD
coldat <- data.frame(colData(sce))

suppressWarnings({
  outl.detected_bottom <- isOutlier(coldat$detected, log = TRUE, nmads = 3, type = "lower", batch = coldat$sample)
  outl.detected_top <- isOutlier(coldat$detected, log = FALSE, nmads = 3, type = "higher", batch = coldat$sample)
  outl.total_bottom <- isOutlier(coldat$total, log = TRUE, nmads = 2, type = "lower", batch = coldat$sample)
  outl.total_top <- isOutlier(coldat$total, log = FALSE, nmads = 2, type = "higher", batch = coldat$sample)
  outl.mito <- isOutlier(coldat$subsets_mito_percent, nmads = 3, log = FALSE, type = "higher", batch = coldat$sample)
  outl.rRNA <- isOutlier(coldat$subsets_rRNA_percent, nmads = 3, log = FALSE, type = "higher", batch = coldat$sample)
})

# Summarize threshold
outl <- ls(pattern = "^outl.[rrna,mito,total,detected]")

qc_thresholds <- lapply(outl, function(x) {
  atr <- attr(get(x), "thresholds")
  nm <- paste(gsub("1", "", strsplit(x, "\\.")[[1]][2]), rownames(atr), sep = "_")
  tmp <- data.frame(Filter = nm, atr)
  tmp <- tmp[!is.infinite(rowSums(tmp[, 2:ncol(tmp), drop = FALSE])), ]
  tmp$Filter <- gsub("_bottom|_top", "", tmp$Filter)
  rownames(tmp) <- NULL
  return(tmp)
}) %>% do.call(rbind, .)

# keep cells that are FALSE in all QC metrics (so not an outlier)
keep_qc <- do.call(cbind, lapply(outl, function(x) data.frame(get(x)) %>% magrittr::set_colnames(x)))
keep_qc[is.na(keep_qc)] <- TRUE
keep_qc <- rowSums(keep_qc) == 0

sce <- sce[, keep_qc]

rm(outl)

# Plots
coldat_f <- data.frame(colData(sce))

qc_detected_genes_before <-
  plotQC(data = coldat, x = "sample", y = "detected", y.lab = "detected genes") + ylim(0, 10000)

qc_detected_genes_after <-
  plotQC(data = coldat_f, x = "sample", y = "detected", y.lab = "detected genes") + ylim(0, 10000)

qc_total_before <-
  plotQC(data = coldat, x = "sample", y = "total", y.lab = "total UMI") + ylim(0, 120000)

qc_total_after <-
  plotQC(data = coldat_f, x = "sample", y = "total", y.lab = "total UMI") + ylim(0, 120000)

qc_mito_before <-
  plotQC(data = coldat, x = "sample", y = "subsets_mito_percent", y.lab = "% mt reads") + ylim(0, 100)

qc_mito_after <-
  plotQC(data = coldat_f, x = "sample", y = "subsets_mito_percent", y.lab = "% mt reads") + ylim(0, 100)

qc_rRNA_before <-
  plotQC(data = coldat, x = "sample", y = "subsets_rRNA_percent", y.lab = "% rRNA reads") + ylim(0, 100)

qc_rRNA_after <-
  plotQC(data = coldat_f, x = "sample", y = "subsets_rRNA_percent", y.lab = "% rRNA reads") + ylim(0, 100)

rm(coldat, coldat_f)

s1b1 <- qc_detected_genes_before # + ggtitle("Detected genes", "before filtering")
s1b2 <- qc_detected_genes_after # + ggtitle("Detected genes", "after filtering")
s1c1 <- qc_total_before # + ggtitle("total UMI counts", "before filtering")
s1c2 <- qc_total_after # + ggtitle("total UMI counts", "before filtering")
s1d1 <- qc_mito_before # + ggtitle("% Alignment to mt genes", "before filtering")
s1d2 <- qc_mito_after # + ggtitle("% Alignment to mt genes", "before filtering")
s1e1 <- qc_rRNA_before # + ggtitle("% Alignment to rRNA genes", "before filtering")
s1e2 <- qc_rRNA_after # + ggtitle("% Alignment to rRNA genes", "before filtering")

Appendix_Figure_S1B <- (s1b1 + gg.noX) / s1b2
Appendix_Figure_S1B

Appendix_Figure_S1C <- (s1c1 + gg.noX) / s1c2
Appendix_Figure_S1C

Appendix_Figure_S1D <- (s1d1 + gg.noX) / s1d2
Appendix_Figure_S1D

Appendix_Figure_S1E <- (s1e1 + gg.noX) / s1e2
Appendix_Figure_S1E

rm(list = ls(pattern = "^s1"))

options(scipen = 0)

# Normalize per batch, then rescale
sce <- bplapply(unique(sce$sample), BPPARAM = bpparam, function(x) {
  s <- sce[, sce$sample == x]

  set.seed(2020)
  q <- scran::quickCluster(x = s, assay.type = "counts", graph.fun = "louvain")

  csf <- scran::computeSumFactors(s, clusters = q)
  lnc <- scuttle::logNormCounts(csf, assay.type = "counts", size_factors = s$sizeFactor)

  return(lnc)
}) %>% do.call(SingleCellExperiment::cbind, .)

sce <- batchelor::multiBatchNorm(sce, batch = sce$sample)

tophvg_exploration <- sapply(X = levels(sce$genotype), FUN = function(gt) {
  s <- sce[, sce$genotype == gt]

  set.seed(2020)
  mgv <- scran::modelGeneVar(x = s, block = factor(s$sample))
  hvg <- getTopHVGs(mgv, n = 1000)

  return(hvg)
}, simplify = FALSE)

# Integration with WT as reference
integration_order <- unlist(lapply(levels(sce$genotype), function(x) grep(x, unique(sce$sample))))

tmp.mnn <- batchelor::fastMNN(sce,
  batch = sce$sample,
  subset.row = unique(unlist(tophvg_exploration)),
  merge.order = integration_order
)

reducedDim(sce, "corrected") <- reducedDim(tmp.mnn, "corrected")

rm(tmp.mnn)

# Clustering & UMAPs
set.seed(2020)
snngraph <- scran::buildSNNGraph(sce, use.dimred = "corrected", type = "jaccard", k = 20)

sce$cluster <- factor(igraph::cluster_louvain(snngraph)$membership)

rm(snngraph)

set.seed(2020)
sce <- scater::runUMAP(sce, dimred = "corrected", min_dist = 0.75, spread = 0.75, n_neighbors = 10, n_threads = 1)

is_G1 <- as.character(c(1, 10, 8, 6, 9, 13, 5))
is_no.G1 <- as.character(c(2, 4, 11, 7))
is_NA <- as.character(c(3, 12)) # these could be genotype-specific as very different cell numbers in cell_numbers

clusters_assigned <- data.frame(Cluster = levels(sce$cluster)) %>%
  dplyr::mutate(cc_group = case_when(
    Cluster %in% is_G1 ~ "G1",
    Cluster %in% is_no.G1 ~ "non.G1",
    Cluster %in% is_NA ~ "NA"
  ))

sce$cc_group <- factor(clusters_assigned$cc_group[match(sce$cluster, clusters_assigned$Cluster)],
  levels = c("G1", "non.G1", "NA")
)

plot_umap(sce, by = "cluster", text_use_label = TRUE, label_size = 5) + theme(legend.position = "none")

plot_umap(sce, gene = "Ly6a")

plot_umap(sce, gene = "Gata1")

plot_umap(sce, gene = "Cebpe")

plot_umap(sce, gene = "Ly86")

plot_umap(sce, gene = "Mki67")

# Only test genes detected in at least 25% of cells of any group
pexpr <- get_pexpr(data = assay(sce), group = sce$cc_group)
pexpr_above_25 <- rowMax(as.matrix(pexpr)) > 25

pairwise_t <- scran::pairwiseTTests(x = sce[pexpr_above_25, ], groups = sce$cc_group, block = sce$genotype, lfc = log(2), direction = "any", BPPARAM = bpparam)

pairwise_t_idx <- data.frame(pairwise_t$pair) %>%
  dplyr::filter(first == "G1" & second == "non.G1") %>%
  rownames() %>%
  as.numeric()

confounding_genes <- as.data.frame(pairwise_t$statistics[[pairwise_t_idx]]) %>%
  tibble::rownames_to_column("Gene") %>%
  dplyr::filter(FDR < 0.05) %>%
  pull(Gene) %>%
  data.frame(name = ., gene_id = gsub("_.*", "", .), gene_name = gsub(".*_", "", .))

knitr::kable(confounding_genes)

dput(confounding_genes$name)

# Clean up and start over on next chunk
reducedDim(sce) <- NULL
sce$cluster <- NULL

# Code for feature selection via sctransform
if (FALSE) {

  # Run sctransform per genotype, regressing batch (=replicate)
  sct_ranked <- mclapply(levels(sce$genotype), mc.cores = mc_workers, function(x) {
    s <- sce[, sce$genotype %in% x]
    cell_attr <- data.frame(batch = factor(as.character(s$sample)), row.names = colnames(s))
    bv <- "batch"

    set.seed(2020)
    i <- suppressMessages(sctransform::vst(
      umi = assay(s, "counts"),
      method = "glmGamPoi",
      verbosity = 0,
      batch_var = bv,
      cell_attr = cell_attr,
      return_gene_attr = TRUE
    )$gene_attr)

    return(i[order(-i$residual_variance), ])
  })
  names(sct_ranked) <- levels(sce$genotype)

  # data.frame with ordered residual variance filtering
  sctransform_vst <- lapply(X = names(sct_ranked), FUN = function(x) {
    y <- sct_ranked[[x]]
    m <- data.frame(Seq = seq(1, nrow(y)), resVar = y$residual_variance)
    m$Genotype <- factor(x)
    return(m[m$Seq < 10000, ])
  }) %>% do.call(rbind, .)

  invisible(gc())

  # Combine the top 1000 HVGs per genotype and remove the confounding genes identified above
  ul <- unlist(lapply(sct_ranked, function(x) head(rownames(x), 1000)))
  tophvg_final <- setdiff(unique(ul), confounding_genes$name)
}

# That is the highly-variable genes from sctransform from the original analysis done on the MacBook as described above in
# the introduction text of this chunk.
tophvg_final <- c(
  "ENSMUSG00000020125_Elane", "ENSMUSG00000056054_S100a8", "ENSMUSG00000027556_Car1",
  "ENSMUSG00000029373_Pf4", "ENSMUSG00000094248_Hist1h2ao", "ENSMUSG00000062456_Rpl9-ps6",
  "ENSMUSG00000066315_Gm12918", "ENSMUSG00000113948_Rpl17-ps3",
  "ENSMUSG00000070343_Gm10288", "ENSMUSG00000056071_S100a9", "ENSMUSG00000084416_Rpl10a-ps1",
  "ENSMUSG00000068240_Gm11808", "ENSMUSG00000069972_Rps13-ps2",
  "ENSMUSG00000024610_Cd74", "ENSMUSG00000089989_Gm45713", "ENSMUSG00000095597_Rps7-ps3",
  "ENSMUSG00000040314_Ctsg", "ENSMUSG00000036594_H2-Aa", "ENSMUSG00000081406_Rps6-ps4",
  "ENSMUSG00000057729_Prtn3", "ENSMUSG00000055093_Gm8430", "ENSMUSG00000057036_Gm7536",
  "ENSMUSG00000050299_Gm9843", "ENSMUSG00000081604_Gm11518", "ENSMUSG00000058603_Rpl28-ps1",
  "ENSMUSG00000025491_Ifitm1", "ENSMUSG00000025014_Dntt", "ENSMUSG00000022584_Ly6c2",
  "ENSMUSG00000009350_Mpo", "ENSMUSG00000100755_Rps23-ps1", "ENSMUSG00000027447_Cst3",
  "ENSMUSG00000022157_Mcpt8", "ENSMUSG00000002985_Apoe", "ENSMUSG00000060586_H2-Eb1",
  "ENSMUSG00000056399_Prss34", "ENSMUSG00000100862_Gm10925", "ENSMUSG00000024681_Ms4a3",
  "ENSMUSG00000073421_H2-Ab1", "ENSMUSG00000001865_Cpa3", "ENSMUSG00000084349_Rpl3-ps1",
  "ENSMUSG00000069682_Gm10275", "ENSMUSG00000073940_Hbb-bt", "ENSMUSG00000027073_Prg2",
  "ENSMUSG00000027562_Car2", "ENSMUSG00000027072_Prg3", "ENSMUSG00000078126_Rpl23a-ps3",
  "ENSMUSG00000050335_Lgals3", "ENSMUSG00000078087_Rps12l1", "ENSMUSG00000069301_H2ac11",
  "ENSMUSG00000060419_Rps16-ps2", "ENSMUSG00000061167_Rpl15-ps3",
  "ENSMUSG00000101249_Gm29216", "ENSMUSG00000061477_Rps7", "ENSMUSG00000090137_Uba52",
  "ENSMUSG00000020644_Id2", "ENSMUSG00000078377_Gm4294", "ENSMUSG00000092341_Malat1",
  "ENSMUSG00000067870_Rpl31-ps8", "ENSMUSG00000069792_Wfdc17",
  "ENSMUSG00000044258_Ctla2a", "ENSMUSG00000067038_Rps12-ps3",
  "ENSMUSG00000083621_Gm14586", "ENSMUSG00000040466_Blvrb", "ENSMUSG00000024680_Ms4a2",
  "ENSMUSG00000030214_Plbd1", "ENSMUSG00000015437_Gzmb", "ENSMUSG00000051748_Wfdc21",
  "ENSMUSG00000026835_Fcnb", "ENSMUSG00000049775_Tmsb4x", "ENSMUSG00000079523_Tmsb10",
  "ENSMUSG00000006360_Crip1", "ENSMUSG00000076490_Trbc1", "ENSMUSG00000038357_Camp",
  "ENSMUSG00000052234_Epx", "ENSMUSG00000055553_Kxd1", "ENSMUSG00000025351_Cd63",
  "ENSMUSG00000080921_Rpl38-ps2", "ENSMUSG00000057262_Rpl15-ps6",
  "ENSMUSG00000040809_Chil3", "ENSMUSG00000030470_Csrp3", "ENSMUSG00000005339_Fcer1a",
  "ENSMUSG00000032484_Ngp", "ENSMUSG00000116835_Gm49594", "ENSMUSG00000028266_Lmo4",
  "ENSMUSG00000102070_Gm28661", "ENSMUSG00000000682_Cd52", "ENSMUSG00000054203_Ifi205",
  "ENSMUSG00000105388_Rpl36a-ps2", "ENSMUSG00000004612_Nkg7", "ENSMUSG00000050621_Rps27rt",
  "ENSMUSG00000034634_Ly6d", "ENSMUSG00000035042_Ccl5", "ENSMUSG00000069919_Hba-a1",
  "ENSMUSG00000106037_Gm4332", "ENSMUSG00000031765_Mt1", "ENSMUSG00000068220_Lgals1",
  "ENSMUSG00000080859_Rpl10-ps1", "ENSMUSG00000071516_H2ac13",
  "ENSMUSG00000031762_Mt2", "ENSMUSG00000023571_C1qtnf12", "ENSMUSG00000029322_Plac8",
  "ENSMUSG00000039109_F13a1", "ENSMUSG00000069270_H2ac6", "ENSMUSG00000086583_Gm15500",
  "ENSMUSG00000078193_Gm2000", "ENSMUSG00000069309_Hist1h2an",
  "ENSMUSG00000110679_Rpl10-ps5", "ENSMUSG00000030413_Pglyrp1",
  "ENSMUSG00000079419_Ms4a6c", "ENSMUSG00000073002_Vamp5", "ENSMUSG00000031722_Hp",
  "ENSMUSG00000100801_Gm15459", "ENSMUSG00000017002_Slpi", "ENSMUSG00000064357_mt-Atp6",
  "ENSMUSG00000003882_Il7r", "ENSMUSG00000052435_Cebpe", "ENSMUSG00000106106_CT010467.1",
  "ENSMUSG00000024397_Aif1", "ENSMUSG00000058135_Gstm1", "ENSMUSG00000001020_S100a4",
  "ENSMUSG00000053063_Clec12a", "ENSMUSG00000022018_Rgcc", "ENSMUSG00000003949_Hlf",
  "ENSMUSG00000030579_Tyrobp", "ENSMUSG00000024399_Ltb", "ENSMUSG00000085442_Gm3362",
  "ENSMUSG00000015937_Macroh2a1", "ENSMUSG00000044285_Ubb-ps",
  "ENSMUSG00000085723_Gm15915", "ENSMUSG00000018819_Lsp1", "ENSMUSG00000063412_Gm10131",
  "ENSMUSG00000026630_Batf3", "ENSMUSG00000045954_Cavin2", "ENSMUSG00000057666_Gapdh",
  "ENSMUSG00000028644_Ermap", "ENSMUSG00000117338_Gm49804", "ENSMUSG00000076757_Trgc4",
  "ENSMUSG00000005474_Myl10", "ENSMUSG00000106588_Gm17590", "ENSMUSG00000001025_S100a6",
  "ENSMUSG00000068407_Rnase12", "ENSMUSG00000038179_Slamf7", "ENSMUSG00000081094_Rpl19-ps11",
  "ENSMUSG00000020077_Srgn", "ENSMUSG00000025163_Cd7", "ENSMUSG00000020857_Nme2",
  "ENSMUSG00000087412_Gm15501", "ENSMUSG00000084106_Gm6136", "ENSMUSG00000076498_Trbc2",
  "ENSMUSG00000034855_Cxcl10", "ENSMUSG00000000782_Tcf7", "ENSMUSG00000032786_Alas1",
  "ENSMUSG00000069302_H2ac12", "ENSMUSG00000076749_Trgc1", "ENSMUSG00000069014_Gm5641",
  "ENSMUSG00000058715_Fcer1g", "ENSMUSG00000094724_Rnaset2b", "ENSMUSG00000078122_F630028O10Rik",
  "ENSMUSG00000021728_Emb", "ENSMUSG00000052305_Hbb-bs", "ENSMUSG00000025492_Ifitm3",
  "ENSMUSG00000003814_Calr", "ENSMUSG00000096006_Gm21596", "ENSMUSG00000066362_Rps13-ps1",
  "ENSMUSG00000047844_Bex4", "ENSMUSG00000058443_Rpl10-ps3", "ENSMUSG00000069516_Lyz2",
  "ENSMUSG00000041481_Serpina3g", "ENSMUSG00000072601_Ear1", "ENSMUSG00000030851_Ldhc",
  "ENSMUSG00000044678_Ly6k", "ENSMUSG00000071052_Rpl7a-ps5", "ENSMUSG00000095687_Rnaset2a",
  "ENSMUSG00000026822_Lcn2", "ENSMUSG00000022504_Ciita", "ENSMUSG00000001270_Ckb",
  "ENSMUSG00000039209_Rpl39l", "ENSMUSG00000015932_Dstn", "ENSMUSG00000015053_Gata2",
  "ENSMUSG00000054072_Iigp1", "ENSMUSG00000079017_Ifi27l2a", "ENSMUSG00000032575_Manf",
  "ENSMUSG00000026728_Vim", "ENSMUSG00000032323_Cyp11a1", "ENSMUSG00000076431_Sox4",
  "ENSMUSG00000004655_Aqp1", "ENSMUSG00000020323_Prss57", "ENSMUSG00000098915_Rpl15-ps2",
  "ENSMUSG00000093798_Gm8355", "ENSMUSG00000044734_Serpinb1a",
  "ENSMUSG00000063556_Gm10132", "ENSMUSG00000053541_Gm4759", "ENSMUSG00000034459_Ifit1",
  "ENSMUSG00000093674_Rpl41", "ENSMUSG00000053310_Nrgn", "ENSMUSG00000004207_Psap",
  "ENSMUSG00000110841_Gpx4-ps2", "ENSMUSG00000023995_Tspo2", "ENSMUSG00000091086_Rpl6l",
  "ENSMUSG00000081051_Gm15427", "ENSMUSG00000032238_Rora", "ENSMUSG00000016494_Cd34",
  "ENSMUSG00000002565_Scin", "ENSMUSG00000074607_Tox2", "ENSMUSG00000075602_Ly6a",
  "ENSMUSG00000031628_Casp3", "ENSMUSG00000030148_Clec4a2", "ENSMUSG00000022309_Angpt1",
  "ENSMUSG00000026536_Ifi211", "ENSMUSG00000059776_Rpl13-ps6",
  "ENSMUSG00000026285_Pdcd1", "ENSMUSG00000076617_Ighm", "ENSMUSG00000027907_S100a11",
  "ENSMUSG00000072596_Ear2", "ENSMUSG00000063286_Gm8995", "ENSMUSG00000072692_Rpl37rt",
  "ENSMUSG00000039997_Ifi203", "ENSMUSG00000026864_Hspa5", "ENSMUSG00000034664_Itga2b",
  "ENSMUSG00000071532_Gm10335", "ENSMUSG00000005583_Mef2c", "ENSMUSG00000031785_Adgrg1",
  "ENSMUSG00000052212_Cd177", "ENSMUSG00000066068_Gm13611", "ENSMUSG00000038393_Txnip",
  "ENSMUSG00000029484_Anxa3", "ENSMUSG00000060509_Xcr1", "ENSMUSG00000041754_Trem3",
  "ENSMUSG00000081855_Rpl17-ps5", "ENSMUSG00000113149_Gm49383",
  "ENSMUSG00000068129_Cst7", "ENSMUSG00000085342_Gm12254", "ENSMUSG00000031838_Ifi30",
  "ENSMUSG00000001930_Vwf", "ENSMUSG00000018102_H2bc4", "ENSMUSG00000032221_Mns1",
  "ENSMUSG00000078350_Smim1", "ENSMUSG00000032503_Arpp21", "ENSMUSG00000024659_Anxa1",
  "ENSMUSG00000021423_Ly86", "ENSMUSG00000059461_Gm7331", "ENSMUSG00000020048_Hsp90b1",
  "ENSMUSG00000064354_mt-Co2", "ENSMUSG00000052681_Rap1b", "ENSMUSG00000024953_Prdx5",
  "ENSMUSG00000054191_Klf1", "ENSMUSG00000059498_Fcgr3", "ENSMUSG00000041959_S100a10",
  "ENSMUSG00000030165_Klrd1", "ENSMUSG00000017144_Rnd3", "ENSMUSG00000028825_Rhd",
  "ENSMUSG00000106549_Gm42653", "ENSMUSG00000047675_Rps8", "ENSMUSG00000029530_Ccr9",
  "ENSMUSG00000113625_Gm3379", "ENSMUSG00000024014_Pim1", "ENSMUSG00000050761_Gp1bb",
  "ENSMUSG00000030785_Cox6a2", "ENSMUSG00000030342_Cd9", "ENSMUSG00000039167_Adgrl4",
  "ENSMUSG00000030654_Arl6ip1", "ENSMUSG00000029413_Naaa", "ENSMUSG00000012405_Rpl15",
  "ENSMUSG00000078606_Gm4070", "ENSMUSG00000021880_Rnase6", "ENSMUSG00000021268_Meg3",
  "ENSMUSG00000054626_Xlr", "ENSMUSG00000057098_Ebf1", "ENSMUSG00000078921_Tgtp2",
  "ENSMUSG00000011752_Pgam1", "ENSMUSG00000031167_Rbm3", "ENSMUSG00000044533_Rps2",
  "ENSMUSG00000100210_H3c7", "ENSMUSG00000101609_Kcnq1ot1", "ENSMUSG00000062270_Morf4l1",
  "ENSMUSG00000007892_Rplp1", "ENSMUSG00000081087_Rps15a-ps7",
  "ENSMUSG00000033427_Upb1", "ENSMUSG00000025290_Rps24", "ENSMUSG00000020044_Timp3",
  "ENSMUSG00000032496_Ltf", "ENSMUSG00000004032_Gstm5", "ENSMUSG00000026418_Tnni1",
  "ENSMUSG00000083716_Gm13436", "ENSMUSG00000042817_Flt3", "ENSMUSG00000070713_Gm10282",
  "ENSMUSG00000082585_Gm15387", "ENSMUSG00000045868_Gvin1", "ENSMUSG00000027669_Gnb4",
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  "ENSMUSG00000095427_Rps2-ps6", "ENSMUSG00000019828_Grm1", "ENSMUSG00000040751_Lat2",
  "ENSMUSG00000030651_Art2b", "ENSMUSG00000018377_Vezf1", "ENSMUSG00000029561_Oasl2",
  "ENSMUSG00000028457_Atp8b5", "ENSMUSG00000101013_A630072M18Rik",
  "ENSMUSG00000047139_Cd24a", "ENSMUSG00000024456_Diaph1", "ENSMUSG00000026074_Map4k4",
  "ENSMUSG00000032584_Mst1r", "ENSMUSG00000104605_Gm42922", "ENSMUSG00000040681_Hmgn1",
  "ENSMUSG00000038489_Polr2l", "ENSMUSG00000025498_Irf7", "ENSMUSG00000024677_Ms4a6b",
  "ENSMUSG00000001128_Cfp", "ENSMUSG00000030742_Lat", "ENSMUSG00000059743_Fdps",
  "ENSMUSG00000023067_Cdkn1a", "ENSMUSG00000051639_Fbl-ps2", "ENSMUSG00000033713_Foxn3",
  "ENSMUSG00000043102_Qrfp", "ENSMUSG00000014599_Csf1", "ENSMUSG00000024910_Ctsw",
  "ENSMUSG00000021760_Gpx8", "ENSMUSG00000017009_Sdc4", "ENSMUSG00000104955_1700016F12Rik",
  "ENSMUSG00000058927_Gm10053", "ENSMUSG00000033082_Clec1a", "ENSMUSG00000027203_Dut",
  "ENSMUSG00000053477_Tcf4", "ENSMUSG00000062901_Klhl24", "ENSMUSG00000015355_Cd48",
  "ENSMUSG00000031870_Pgr", "ENSMUSG00000034833_Tespa1", "ENSMUSG00000003420_Fcgrt",
  "ENSMUSG00000022659_Gcsam", "ENSMUSG00000018362_Kpna2", "ENSMUSG00000084085_Gm16140",
  "ENSMUSG00000004897_Hdgf", "ENSMUSG00000115338_Pnp", "ENSMUSG00000061390_Uba52-ps",
  "ENSMUSG00000035569_Ankrd11", "ENSMUSG00000079293_Clec7a", "ENSMUSG00000025486_Sirt3",
  "ENSMUSG00000026385_Dbi", "ENSMUSG00000039145_Camk1d", "ENSMUSG00000036181_H1f2",
  "ENSMUSG00000062825_Actg1", "ENSMUSG00000053907_Mat2a", "ENSMUSG00000068349_Gml",
  "ENSMUSG00000042606_Hirip3", "ENSMUSG00000022952_Runx1", "ENSMUSG00000038623_Tm6sf1",
  "ENSMUSG00000099927_Gm8226", "ENSMUSG00000067147_Rpl7a-ps11",
  "ENSMUSG00000071637_Cebpd", "ENSMUSG00000030325_Klrb1c", "ENSMUSG00000082292_Gm12250",
  "ENSMUSG00000042770_Hebp1", "ENSMUSG00000113061_Rps18-ps5", "ENSMUSG00000020737_Jpt1",
  "ENSMUSG00000112605_Gm7476", "ENSMUSG00000029673_Auts2", "ENSMUSG00000074800_Gm4149",
  "ENSMUSG00000032966_Fkbp1a", "ENSMUSG00000035085_1700020L24Rik",
  "ENSMUSG00000020846_Rflnb", "ENSMUSG00000015312_Gadd45b", "ENSMUSG00000052727_Map1b",
  "ENSMUSG00000050473_Slc35d3", "ENSMUSG00000038508_Gdf15", "ENSMUSG00000022475_Hdac7",
  "ENSMUSG00000002028_Kmt2a", "ENSMUSG00000066407_Gm10263", "ENSMUSG00000026712_Mrc1",
  "ENSMUSG00000026938_Fcna", "ENSMUSG00000081058_H3c15", "ENSMUSG00000005672_Kit",
  "ENSMUSG00000069793_Slfn9", "ENSMUSG00000031780_Ccl17", "ENSMUSG00000024965_Fermt3",
  "ENSMUSG00000107176_Gm9794", "ENSMUSG00000102332_Gm19331", "ENSMUSG00000060572_Mfap2",
  "ENSMUSG00000067212_H2-T23", "ENSMUSG00000044867_Gimap1os", "ENSMUSG00000026580_Selp",
  "ENSMUSG00000030844_Rgs10", "ENSMUSG00000042750_Bex2", "ENSMUSG00000082901_Rps8-ps5",
  "ENSMUSG00000023944_Hsp90ab1", "ENSMUSG00000042207_Kdm5b", "ENSMUSG00000025804_Ccr1",
  "ENSMUSG00000030094_Xpc", "ENSMUSG00000078671_Chd2", "ENSMUSG00000020732_Rab37",
  "ENSMUSG00000031613_Hpgd", "ENSMUSG00000081740_Gm14279", "ENSMUSG00000038872_Zfhx3",
  "ENSMUSG00000028961_Pgd", "ENSMUSG00000027342_Pcna", "ENSMUSG00000040732_Erg",
  "ENSMUSG00000092274_Neat1", "ENSMUSG00000079563_Pglyrp2", "ENSMUSG00000026360_Rgs2",
  "ENSMUSG00000105867_Gm42517", "ENSMUSG00000110902_Gm33104", "ENSMUSG00000002014_Ssr4",
  "ENSMUSG00000005533_Igf1r", "ENSMUSG00000036461_Elf1", "ENSMUSG00000117442_1810073O08Rik",
  "ENSMUSG00000037664_Cdkn1c", "ENSMUSG00000037138_Aff3", "ENSMUSG00000036186_Dipk1b",
  "ENSMUSG00000002107_Celf2", "ENSMUSG00000071303_Rps8-ps1", "ENSMUSG00000042757_Tmem108",
  "ENSMUSG00000039196_Orm1", "ENSMUSG00000022651_Retnlg", "ENSMUSG00000005054_Cstb",
  "ENSMUSG00000071561_Cstdc5", "ENSMUSG00000029359_Tesc", "ENSMUSG00000033981_Gria2",
  "ENSMUSG00000022225_Cma1", "ENSMUSG00000021702_Thbs4", "ENSMUSG00000024245_Tmem178",
  "ENSMUSG00000013974_Mcemp1", "ENSMUSG00000071562_Stfa1", "ENSMUSG00000071715_Ncf4",
  "ENSMUSG00000036256_Igfbp7", "ENSMUSG00000072940_Gm10443", "ENSMUSG00000041324_Inhba",
  "ENSMUSG00000022582_Ly6g", "ENSMUSG00000000157_Itgb2l", "ENSMUSG00000022026_Olfm4",
  "ENSMUSG00000074272_Ceacam1", "ENSMUSG00000096054_Syne1", "ENSMUSG00000067149_Jchain",
  "ENSMUSG00000029304_Spp1", "ENSMUSG00000033220_Rac2", "ENSMUSG00000046727_Cystm1",
  "ENSMUSG00000046180_4930550L24Rik", "ENSMUSG00000063856_Gpx1",
  "ENSMUSG00000019987_Arg1", "ENSMUSG00000059956_Serpinb12", "ENSMUSG00000082475_Gm7206",
  "ENSMUSG00000024907_Gal", "ENSMUSG00000028603_Scp2", "ENSMUSG00000019851_Perp",
  "ENSMUSG00000031397_Tktl1", "ENSMUSG00000106874_Gm20186", "ENSMUSG00000053279_Aldh1a1",
  "ENSMUSG00000045034_Ankrd34b", "ENSMUSG00000117380_E430002N23Rik",
  "ENSMUSG00000030711_Sult1a1", "ENSMUSG00000033083_Tbc1d4", "ENSMUSG00000026070_Il18r1",
  "ENSMUSG00000023132_Gzma", "ENSMUSG00000021879_Dnah12", "ENSMUSG00000022037_Clu",
  "ENSMUSG00000030142_Clec4e", "ENSMUSG00000039081_Zfp503", "ENSMUSG00000025064_Col17a1",
  "ENSMUSG00000079186_Gzmc", "ENSMUSG00000053846_Lipg", "ENSMUSG00000089901_Gm8113",
  "ENSMUSG00000003379_Cd79a", "ENSMUSG00000054169_Ceacam10", "ENSMUSG00000030231_Plekha5",
  "ENSMUSG00000078922_Tgtp1", "ENSMUSG00000047953_Gp5", "ENSMUSG00000019194_Scn1b",
  "ENSMUSG00000087075_Lbhd2", "ENSMUSG00000075420_Smim6", "ENSMUSG00000055817_Mta3",
  "ENSMUSG00000068806_Olfr1259", "ENSMUSG00000015950_Ncf1", "ENSMUSG00000082141_Gm11212",
  "ENSMUSG00000022528_Hes1", "ENSMUSG00000027611_Procr", "ENSMUSG00000029372_Ppbp",
  "ENSMUSG00000054435_Gimap4", "ENSMUSG00000062210_Tnfaip8", "ENSMUSG00000042745_Id1",
  "ENSMUSG00000001225_Slc26a3", "ENSMUSG00000030775_Trat1", "ENSMUSG00000098240_Gm4575",
  "ENSMUSG00000052396_Mogat2", "ENSMUSG00000023333_Gcm1", "ENSMUSG00000091472_Gm3739",
  "ENSMUSG00000022378_Fam49b", "ENSMUSG00000004864_Mapk13", "ENSMUSG00000116876_Gm49721",
  "ENSMUSG00000020407_Upp1", "ENSMUSG00000046447_Camk2n1", "ENSMUSG00000047880_Cxcr5",
  "ENSMUSG00000096472_Cdkn2d", "ENSMUSG00000049577_Zfpm1", "ENSMUSG00000028184_Adgrl2",
  "ENSMUSG00000066682_Pilrb2", "ENSMUSG00000051839_Gypa", "ENSMUSG00000050552_Lamtor4",
  "ENSMUSG00000051022_Hs3st1", "ENSMUSG00000054871_Tmem158", "ENSMUSG00000117485_Gm19696",
  "ENSMUSG00000002033_Cd3g", "ENSMUSG00000061143_Maml3", "ENSMUSG00000039899_Fgl2",
  "ENSMUSG00000056025_Clca3a1", "ENSMUSG00000030786_Itgam", "ENSMUSG00000070803_Cited4",
  "ENSMUSG00000059970_Hspa2", "ENSMUSG00000085629_Gm11697", "ENSMUSG00000029661_Col1a2",
  "ENSMUSG00000066026_Dhrs3", "ENSMUSG00000117098_Gm49909", "ENSMUSG00000026922_Agpat2",
  "ENSMUSG00000118196_Gm29946", "ENSMUSG00000047361_Gm973", "ENSMUSG00000064109_Hcst",
  "ENSMUSG00000074874_Ctla2b", "ENSMUSG00000049907_Rasl11b", "ENSMUSG00000036427_Gpi1",
  "ENSMUSG00000038903_Ccdc68", "ENSMUSG00000029379_Cxcl3", "ENSMUSG00000042700_Sipa1l1",
  "ENSMUSG00000021200_Asb2", "ENSMUSG00000029275_Gfi1", "ENSMUSG00000040747_Cd53",
  "ENSMUSG00000038463_Olfml2b", "ENSMUSG00000029096_Htra3", "ENSMUSG00000032311_Nrg4",
  "ENSMUSG00000040026_Saa3", "ENSMUSG00000029915_Clec5a", "ENSMUSG00000045569_Mc2r",
  "ENSMUSG00000022443_Myh9", "ENSMUSG00000062393_Dgkk", "ENSMUSG00000030399_Ckm",
  "ENSMUSG00000021701_Plk2", "ENSMUSG00000042751_Nmnat2", "ENSMUSG00000038893_Fam117a",
  "ENSMUSG00000017737_Mmp9", "ENSMUSG00000031451_Gas6", "ENSMUSG00000027221_Chst1",
  "ENSMUSG00000032905_Atg12", "ENSMUSG00000020609_Apob", "ENSMUSG00000035227_Spcs2",
  "ENSMUSG00000034607_Pof1b", "ENSMUSG00000074141_Il4i1", "ENSMUSG00000035021_Baz1a",
  "ENSMUSG00000016024_Lbp", "ENSMUSG00000054619_Mettl7a1", "ENSMUSG00000103800_Pcdha8",
  "ENSMUSG00000049149_Olfr1258", "ENSMUSG00000018593_Sparc", "ENSMUSG00000026594_Ralgps2",
  "ENSMUSG00000097221_1810049J17Rik", "ENSMUSG00000049313_Sorl1",
  "ENSMUSG00000026604_Ptpn14", "ENSMUSG00000036816_Atoh7", "ENSMUSG00000104793_Gm43756",
  "ENSMUSG00000028717_Tal1", "ENSMUSG00000067235_H2-Q10", "ENSMUSG00000058799_Nap1l1",
  "ENSMUSG00000026069_Il1rl1", "ENSMUSG00000049130_C5ar1", "ENSMUSG00000073676_Hspe1",
  "ENSMUSG00000020388_Pdlim4", "ENSMUSG00000118653_AC159819.1",
  "ENSMUSG00000074336_Apoc4", "ENSMUSG00000046959_Slc26a1", "ENSMUSG00000026009_Icos",
  "ENSMUSG00000101739_Gm5733", "ENSMUSG00000067613_Krt83", "ENSMUSG00000051650_B3gnt2",
  "ENSMUSG00000042354_Gnl3", "ENSMUSG00000057058_Skap1", "ENSMUSG00000046841_Ckap4",
  "ENSMUSG00000032556_Bfsp2", "ENSMUSG00000055435_Maf", "ENSMUSG00000026893_Gca",
  "ENSMUSG00000037710_Cisd1", "ENSMUSG00000076609_Igkc", "ENSMUSG00000045763_Basp1",
  "ENSMUSG00000027239_Mdk", "ENSMUSG00000027808_Serp1", "ENSMUSG00000029866_Kel",
  "ENSMUSG00000023926_Rhag", "ENSMUSG00000086320_Gm12840", "ENSMUSG00000001946_Esam",
  "ENSMUSG00000021614_Vcan", "ENSMUSG00000041859_Mcm3", "ENSMUSG00000025245_Lztfl1",
  "ENSMUSG00000031355_Arhgap6", "ENSMUSG00000093507_Gm20627", "ENSMUSG00000104876_Trdc",
  "ENSMUSG00000021676_Iqgap2", "ENSMUSG00000034881_Tbxa2r", "ENSMUSG00000008193_Spib",
  "ENSMUSG00000030671_Pde3b", "ENSMUSG00000021591_Glrx", "ENSMUSG00000032011_Thy1",
  "ENSMUSG00000035000_Dpp4", "ENSMUSG00000038037_Socs1", "ENSMUSG00000055322_Tns1",
  "ENSMUSG00000030054_Gp9", "ENSMUSG00000038227_Hoxa9", "ENSMUSG00000024587_Nars",
  "ENSMUSG00000039542_Ncam1", "ENSMUSG00000038604_Ripor1", "ENSMUSG00000061603_Akap6",
  "ENSMUSG00000098650_Commd1b", "ENSMUSG00000040462_Os9", "ENSMUSG00000071415_Rpl23",
  "ENSMUSG00000025746_Il6", "ENSMUSG00000040822_1700123O20Rik",
  "ENSMUSG00000025104_Hdgfl3", "ENSMUSG00000027496_Aurka", "ENSMUSG00000027636_Sla2",
  "ENSMUSG00000056643_Chst13", "ENSMUSG00000024620_Pdgfrb", "ENSMUSG00000025724_Sec11a",
  "ENSMUSG00000063415_Cyp26b1", "ENSMUSG00000043207_Zmpste24",
  "ENSMUSG00000074269_Rec114", "ENSMUSG00000039529_Atp8b1", "ENSMUSG00000043557_Mdga1",
  "ENSMUSG00000027199_Gatm", "ENSMUSG00000116615_4930548J01Rik",
  "ENSMUSG00000044629_Cnrip1", "ENSMUSG00000027763_Mbnl1", "ENSMUSG00000063232_Serpina11",
  "ENSMUSG00000044055_Otos", "ENSMUSG00000024608_Rps14", "ENSMUSG00000024793_Tnfrsf25",
  "ENSMUSG00000076755_Trgv1", "ENSMUSG00000042284_Itga1", "ENSMUSG00000033554_Dph5",
  "ENSMUSG00000027347_Rasgrp1", "ENSMUSG00000029204_Rhoh", "ENSMUSG00000025503_Taldo1",
  "ENSMUSG00000102154_Gm37469", "ENSMUSG00000012422_Tmem167", "ENSMUSG00000019817_Plagl1",
  "ENSMUSG00000046245_Pilra", "ENSMUSG00000096719_Mrgpra2b", "ENSMUSG00000027030_Stk39",
  "ENSMUSG00000027331_Knstrn", "ENSMUSG00000105945_Gm43570", "ENSMUSG00000030559_Rab38",
  "ENSMUSG00000022892_App", "ENSMUSG00000030000_Add2", "ENSMUSG00000071714_Csf2rb2",
  "ENSMUSG00000034755_Pcdh11x", "ENSMUSG00000028228_Cpne3", "ENSMUSG00000063450_Syne2",
  "ENSMUSG00000028927_Padi2", "ENSMUSG00000020484_Xbp1", "ENSMUSG00000033192_Lpcat2",
  "ENSMUSG00000001763_Tspan33", "ENSMUSG00000020717_Pecam1", "ENSMUSG00000025777_Gdap1",
  "ENSMUSG00000104068_Gm37199", "ENSMUSG00000062646_Ganc", "ENSMUSG00000006589_Aprt",
  "ENSMUSG00000028464_Tpm2", "ENSMUSG00000030617_Ccdc83", "ENSMUSG00000051339_2900026A02Rik",
  "ENSMUSG00000049350_Zg16", "ENSMUSG00000020689_Itgb3", "ENSMUSG00000015316_Slamf1",
  "ENSMUSG00000022226_Mcpt2", "ENSMUSG00000099980_Gm5619", "ENSMUSG00000041355_Ssr2",
  "ENSMUSG00000029860_Zyx", "ENSMUSG00000029553_Tfec", "ENSMUSG00000026473_Glul",
  "ENSMUSG00000075701_Selenos", "ENSMUSG00000090019_Gimap1", "ENSMUSG00000071547_Nt5dc2",
  "ENSMUSG00000036523_Greb1", "ENSMUSG00000022868_Ahsg", "ENSMUSG00000086763_Plxna4os1",
  "ENSMUSG00000066672_Olfr417", "ENSMUSG00000056091_St3gal5", "ENSMUSG00000069873_4930438A08Rik",
  "ENSMUSG00000026655_Fam107b", "ENSMUSG00000023010_Tmbim6", "ENSMUSG00000034936_Arl4d",
  "ENSMUSG00000028332_Hemgn", "ENSMUSG00000004473_Clec11a", "ENSMUSG00000040998_Npnt",
  "ENSMUSG00000004665_Cnn2", "ENSMUSG00000049493_Pls1", "ENSMUSG00000085912_Trp53cor1",
  "ENSMUSG00000037235_Mxd4", "ENSMUSG00000004961_Syt5", "ENSMUSG00000022346_Myc",
  "ENSMUSG00000021485_Mxd3", "ENSMUSG00000085786_Gm15987", "ENSMUSG00000104806_Gm42566",
  "ENSMUSG00000015843_Rxrg", "ENSMUSG00000032657_Fam189b", "ENSMUSG00000042462_Dctpp1",
  "ENSMUSG00000034957_Cebpa", "ENSMUSG00000068606_Gm4841", "ENSMUSG00000021403_Serpinb9b",
  "ENSMUSG00000116652_B830017H08Rik", "ENSMUSG00000026463_Atp2b4",
  "ENSMUSG00000018920_Cxcl16", "ENSMUSG00000029563_Foxp2", "ENSMUSG00000031497_Tnfsf13b"
)

# Integration
set.seed(2020)
tmp <- batchelor::fastMNN(sce[tophvg_final, ], batch = sce$sample, merge.order = integration_order)
reducedDim(sce, "corrected") <- reducedDim(tmp, "corrected")
rm(tmp)

# Clustering
set.seed(2020)
snngraph <- scran::buildSNNGraph(sce, use.dimred = "corrected", type = "jaccard", k = 10)

sce$cluster <- factor(igraph::cluster_louvain(snngraph)$membership)
rm(snngraph)

# UMAP
set.seed(2020)
sce <- scater::runUMAP(sce, dimred = "corrected", min_dist = .5, spread = .5)

# This is how the code must look to be reproducible, with RNGseed!
if (FALSE) {

  # This is how we did incorrectly run it, using set.seed()
  lb <- length(unique(sce$sample))
  bp <- BiocParallel::MulticoreParam(ifelse(mc_workers > lb, lb, mc_workers), RNGseed = 1) # <=  RNGseed!!!!! is critical !!!!!

  set.seed(2020)
  sce <- scDblFinder::scDblFinder(
    sce = sce, clusters = as.character(sce$cluster),
    samples = sce$sample, BPPARAM = bp
  )

  # This is how it would have been correct, using RNGseed
  lb <- length(unique(sce$sample))
  bp <- BiocParallel::MulticoreParam(ifelse(mc_workers > lb, lb, mc_workers), RNGseed = 1)
  sce <- scDblFinder(sce, clusters = "cluster", samples = "sample", BPPARAM = bp)
}

# Here the dput of the original output of scDblFinder with cells blacklisted as doublets
is_doublet_cells <- c(
  "URE_rep1_cell233", "URE_rep1_cell1914", "URE_rep1_cell3963",
  "URE_rep1_cell4143", "URE_rep2_cell634", "WT_rep1_cell19", "WT_rep1_cell540",
  "WT_rep1_cell1826", "WT_rep1_cell1898", "WT_rep2_cell7", "URE_rep1_cell162",
  "URE_rep1_cell163", "URE_rep1_cell167", "URE_rep1_cell170", "URE_rep1_cell194",
  "URE_rep1_cell195", "URE_rep1_cell197", "URE_rep1_cell206", "URE_rep1_cell207",
  "URE_rep1_cell209", "URE_rep1_cell210", "URE_rep1_cell222", "URE_rep1_cell227",
  "URE_rep1_cell240", "URE_rep1_cell241", "URE_rep1_cell246", "URE_rep1_cell253",
  "URE_rep1_cell254", "URE_rep1_cell255", "URE_rep1_cell270", "URE_rep1_cell277",
  "URE_rep1_cell278", "URE_rep1_cell299", "URE_rep1_cell302", "URE_rep1_cell315",
  "URE_rep1_cell324", "URE_rep1_cell328", "URE_rep1_cell331", "URE_rep1_cell332",
  "URE_rep1_cell335", "URE_rep1_cell349", "URE_rep1_cell351", "URE_rep1_cell377",
  "URE_rep1_cell378", "URE_rep1_cell379", "URE_rep1_cell381", "URE_rep1_cell384",
  "URE_rep1_cell385", "URE_rep1_cell393", "URE_rep1_cell400", "URE_rep1_cell402",
  "URE_rep1_cell417", "URE_rep1_cell423", "URE_rep1_cell428", "URE_rep1_cell429",
  "URE_rep1_cell436", "URE_rep1_cell441", "URE_rep1_cell442", "URE_rep1_cell446",
  "URE_rep1_cell449", "URE_rep1_cell451", "URE_rep1_cell460", "URE_rep1_cell465",
  "URE_rep1_cell467", "URE_rep1_cell479", "URE_rep1_cell481", "URE_rep1_cell482",
  "URE_rep1_cell484", "URE_rep1_cell488", "URE_rep1_cell491", "URE_rep1_cell500",
  "URE_rep1_cell507", "URE_rep1_cell520", "URE_rep1_cell522", "URE_rep1_cell527",
  "URE_rep1_cell531", "URE_rep1_cell546", "URE_rep1_cell548", "URE_rep1_cell554",
  "URE_rep1_cell555", "URE_rep1_cell556", "URE_rep1_cell570", "URE_rep1_cell577",
  "URE_rep1_cell587", "URE_rep1_cell588", "URE_rep1_cell591", "URE_rep1_cell608",
  "URE_rep1_cell636", "URE_rep1_cell654", "URE_rep1_cell662", "URE_rep1_cell666",
  "URE_rep1_cell668", "URE_rep1_cell671", "URE_rep1_cell680", "URE_rep1_cell683",
  "URE_rep1_cell687", "URE_rep1_cell694", "URE_rep1_cell695", "URE_rep1_cell700",
  "URE_rep1_cell701", "URE_rep1_cell705", "URE_rep1_cell708", "URE_rep1_cell718",
  "URE_rep1_cell719", "URE_rep1_cell721", "URE_rep1_cell726", "URE_rep1_cell743",
  "URE_rep1_cell745", "URE_rep1_cell747", "URE_rep1_cell754", "URE_rep1_cell762",
  "URE_rep1_cell765", "URE_rep1_cell775", "URE_rep1_cell784", "URE_rep1_cell790",
  "URE_rep1_cell793", "URE_rep1_cell797", "URE_rep1_cell798", "URE_rep1_cell800",
  "URE_rep1_cell802", "URE_rep1_cell806", "URE_rep1_cell810", "URE_rep1_cell812",
  "URE_rep1_cell814", "URE_rep1_cell816", "URE_rep1_cell833", "URE_rep1_cell835",
  "URE_rep1_cell846", "URE_rep1_cell850", "URE_rep1_cell866", "URE_rep1_cell874",
  "URE_rep1_cell897", "URE_rep1_cell904", "URE_rep1_cell908", "URE_rep1_cell924",
  "URE_rep1_cell939", "URE_rep1_cell953", "URE_rep1_cell957", "URE_rep1_cell959",
  "URE_rep1_cell971", "URE_rep1_cell972", "URE_rep1_cell979", "URE_rep1_cell986",
  "URE_rep1_cell988", "URE_rep1_cell991", "URE_rep1_cell992", "URE_rep1_cell1001",
  "URE_rep1_cell1002", "URE_rep1_cell1027", "URE_rep1_cell1043",
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  "WT_rep2_cell694", "WT_rep2_cell698", "WT_rep2_cell699", "WT_rep2_cell707",
  "WT_rep2_cell711", "WT_rep2_cell722", "WT_rep2_cell729", "WT_rep2_cell735",
  "WT_rep2_cell743", "WT_rep2_cell763", "WT_rep2_cell765", "WT_rep2_cell769",
  "WT_rep2_cell782", "WT_rep2_cell792", "WT_rep2_cell828", "WT_rep2_cell833",
  "WT_rep2_cell849", "WT_rep2_cell866", "WT_rep2_cell874", "WT_rep2_cell883",
  "WT_rep2_cell894", "WT_rep2_cell909", "WT_rep2_cell916", "WT_rep2_cell923",
  "WT_rep2_cell929", "WT_rep2_cell951", "WT_rep2_cell968", "WT_rep2_cell1005",
  "WT_rep2_cell1062", "WT_rep2_cell1073", "WT_rep2_cell1083", "WT_rep2_cell1091",
  "WT_rep2_cell1117", "WT_rep2_cell1137", "WT_rep2_cell1140", "WT_rep2_cell1184",
  "WT_rep2_cell1186", "WT_rep2_cell1187", "WT_rep2_cell1191", "WT_rep2_cell1199",
  "WT_rep2_cell1200", "WT_rep2_cell1218", "WT_rep2_cell1222", "WT_rep2_cell1236",
  "WT_rep2_cell1245", "WT_rep2_cell1291", "WT_rep2_cell1310", "WT_rep2_cell1321",
  "WT_rep2_cell1329", "WT_rep2_cell1348", "WT_rep2_cell1408", "WT_rep2_cell1420",
  "WT_rep2_cell1486", "WT_rep2_cell1513", "WT_rep2_cell1566", "WT_rep2_cell1584",
  "WT_rep2_cell1585", "WT_rep2_cell1599", "WT_rep2_cell1624", "WT_rep2_cell1634",
  "WT_rep2_cell1650", "WT_rep2_cell1668", "WT_rep2_cell1702", "WT_rep2_cell1713",
  "WT_rep2_cell1730", "WT_rep2_cell1743", "WT_rep2_cell1746", "WT_rep2_cell1780",
  "WT_rep2_cell1781", "WT_rep2_cell1801", "WT_rep2_cell1821", "WT_rep2_cell1832",
  "WT_rep2_cell1844", "WT_rep2_cell1892", "WT_rep2_cell1899", "WT_rep2_cell1971",
  "WT_rep2_cell1972", "WT_rep2_cell1975", "WT_rep2_cell1994", "WT_rep2_cell2048",
  "WT_rep2_cell2069", "WT_rep2_cell2075", "WT_rep2_cell2094", "WT_rep2_cell2121",
  "WT_rep2_cell2134", "WT_rep2_cell2163", "WT_rep2_cell2200", "WT_rep2_cell2204",
  "WT_rep2_cell2284", "WT_rep2_cell2324", "WT_rep2_cell2350", "WT_rep2_cell2351",
  "WT_rep2_cell2379", "WT_rep2_cell2432", "WT_rep2_cell2450", "WT_rep2_cell2524",
  "WT_rep2_cell2608", "WT_rep2_cell2630", "WT_rep2_cell2652", "WT_rep2_cell2709",
  "WT_rep2_cell2714", "WT_rep2_cell2833", "WT_rep2_cell2834", "WT_rep2_cell2893",
  "WT_rep2_cell3065", "WT_rep2_cell3236", "WT_rep2_cell3249", "WT_rep2_cell3359",
  "WT_rep2_cell3494", "WT_rep2_cell3553", "WT_rep2_cell3635", "WT_rep2_cell3695",
  "WT_rep2_cell4114"
)

# One tiny cluster was identified as dendritic cells, remove this as not focus of out study and few cells so low power
plot_umap(sce, gene = "Cd74") | (plot_umap(sce, by = "cluster", text_add = FALSE) + theme(legend.position = "none"))

cd74_all <-
  data.frame(
    value = as.numeric(assay(sce["ENSMUSG00000024610_Cd74", ], "logcounts")[1, ]),
    cluster = sce$cluster
  ) %>%
  dplyr::mutate(isexpr = if_else(value > 0, TRUE, FALSE)) %>%
  dplyr::group_by(cluster) %>%
  dplyr::summarize(percent = 100 * sum(isexpr) / length(isexpr)) %>%
  data.frame()

is_dc_cells <-
  cd74_all %>%
  dplyr::filter(percent > 99) %>%
  dplyr::select(cluster) %>%
  dplyr::left_join(x = ., y = data.frame(colData(sce), cell_name = colnames(sce)), by = "cluster") %>%
  pull(cell_name)

# Combine DCs and doublets into a vector of cells to be removed
rm.cells <- c(is_doublet_cells, is_dc_cells)

sce <- sce[, !colnames(sce) %in% rm.cells]

# Now with all unwanted cells remove produce the final clustering and UMAP for downstream analysis
set.seed(2020)
snngraph <- scran::buildSNNGraph(sce, use.dimred = "corrected", type = "jaccard", k = 20)

sce$cluster <- factor(igraph::cluster_louvain(snngraph)$membership)
rm(snngraph)

set.seed(2020)
sce <- scater::runUMAP(sce, dimred = "corrected", min_dist = .5, spread = .5)

# We then renamed the clusters post-hoc so they're developmentally chronological
df.rename <- data.frame(Old = levels(sce$cluster), New = c("C2", "C7", "C4", "C5", "C6", "C1", "C3", "C8"))
sce$cluster <- factor(df.rename$New[match(as.character(sce$cluster), df.rename$Old)],
  levels = paste0("C", seq(1, max(as.numeric(gsub("C", "", as.character(df.rename$New))))))
)

sce$cg <- factor(paste(sce$genotype, sce$cluster, sep = "_"))

# UMAP by cluster:
Figure_1A <- plot_umap(sce, by = "cluster", text_add = TRUE, text_use_label = TRUE, label_size = 5) +
  theme(legend.position = "none") +
  coord_fixed()

Figure_1A

# Pseudobulk aggregation by cluster, replicate and genotype
aggregate_by <- c("cluster", "genotype", "replicate")
summed <- scuttle::aggregateAcrossCells(sce, id = colData(sce)[, aggregate_by])
colnames(summed) <- paste(summed$genotype, summed$cluster, summed$replicate, sep = "_")

gf <- factor(paste(sce$genotype, sce$cluster, sep = "_"))
pseudobulk_percent_expressed <- get_pexpr(data = assay(sce, "counts"), group = as.character(gf))

# Build a DESeqDataSet with a full-factorial design based on the pseudobulks
use_counts <- round(assay(summed, "counts"))
mode(use_counts) <- "integer"
group_factor <- factor(gsub("_rep.*", "", colnames(summed)))

dds_pseudobulk <- DESeq2::DESeqDataSetFromMatrix(
  countData = use_counts,
  colData = DataFrame(
    group = group_factor,
    genotype = factor(summed$genotype),
    cluster = factor(summed$cluster)
  ),
  design = ~group
)

rm(use_counts, summed, group_factor)

# Exclude the clusters with notably different cell numbers between genotypes
dds_pseudobulk <- dds_pseudobulk[, !dds_pseudobulk$group %in% c("WT_C4", "URE_C5")]
colData(dds_pseudobulk) <- droplevels.data.frame(colData(dds_pseudobulk))

# As a first prefilter, only keep genes that are expressed by at least 10% of cells per factorial group
dds_pseudobulk <- dds_pseudobulk[rowSums(pseudobulk_percent_expressed[, levels(dds_pseudobulk$group)] >= 10) > 0, ]

# Normalize pseudobulks with DESeq2
dds_pseudobulk <- DESeq2::estimateSizeFactors(dds_pseudobulk)

# Use rlog downstream for clustering of the URE vs WT DEGs
rld <- assay(DESeq2::rlog(dds_pseudobulk, blind = FALSE))

lvl_genotype <- levels(dds_pseudobulk$genotype)

pseudobulk_markers <- lapply(lvl_genotype, function(g) {

  # Test clusters all vs all
  dds <- dds_pseudobulk[, dds_pseudobulk$genotype %in% g]
  colData(dds) <- droplevels.data.frame(colData(dds))
  design(dds) <- ~cluster
  dds <- DESeq2::DESeq(dds, quiet = TRUE)

  lvl_cluster <- levels(droplevels(dds$cluster))

  all_contrasts <- combn(lvl_cluster, 2, function(x) c("cluster", x), simplify = FALSE)
  all_contrasts <- c(all_contrasts, lapply(all_contrasts, function(x) c(x[1], x[3], x[2])))

  markers <- mclapply(all_contrasts, mc.cores = mc_workers, function(co) {
    res <- DESeq2::results(object = dds, contrast = co, alpha = 0.05)
    lfc <- as.data.frame(DESeq2::lfcShrink(dds = dds, contrast = co, type = "ashr", quiet = TRUE))
    rn <- rownames(lfc[lfc$log2FoldChange > log2(1.5) & lfc$padj < 0.05, ])
    cl <- co[2]
    keep25 <- pseudobulk_percent_expressed[, paste0(g, "_", cl), drop = TRUE] >= 25
    final <- intersect(names(keep25[keep25]), rn)

    return(final)
  })

  # We have 7 clusters, so testing 1 versus 6 others and require that a gene must be overexpressed
  # in the given versus all other clusters
  min_n <- length(lvl_cluster) - 1
  markers <- sapply(lvl_cluster, function(cl) {
    u <- which(unlist(lapply(all_contrasts, function(x) x[2] == cl)))
    tbl <- table(unlist(markers[u]))
    m <- tbl == min_n
    names(m[m])
  }, simplify = FALSE)

  names(markers) <- paste0(g, "_", names(markers))

  return(markers)
})

pseudobulk_markers <- c(pseudobulk_markers[[1]], pseudobulk_markers[[2]])

lengths(pseudobulk_markers)

Dataset_EV1 <- sapply(names(pseudobulk_markers), function(x) {
  genotype <- gsub("_.*", "", x)
  current <- pseudobulk_markers[[x]]

  first <- as.numeric(pseudobulk_percent_expressed[current, x, drop = TRUE])

  names_second <- setdiff(grep(genotype, names(pseudobulk_markers), value = TRUE), x)
  second <- as.numeric(apply(pseudobulk_percent_expressed[current, names_second], 1, mean))

  d <- data.frame(gene = current, pct.1 = first, pct.average.rest = second)

  return(d)
}, simplify = FALSE)

openxlsx::write.xlsx(x = Dataset_EV1, file = paste0(outdir, "/Dataset_EV1_raw.xlsx"), overwrite = TRUE)

dds_pseudobulk <- DESeq2::DESeq(dds_pseudobulk, quiet = TRUE)
dds_pseudobulk$group <- droplevels(dds_pseudobulk$group)
rownames_dds_ure_wt <- rownames(dds_pseudobulk)

ure_wt_contrasts <- lapply(c("C1", "C2", "C3"), function(x) c("group", paste0("URE_", x), paste0("WT_", x)))

ure_wt_degs <- lapply(ure_wt_contrasts, function(x) {
  tt <- DESeq2::results(
    object = dds_pseudobulk, contrast = x,
    independentFiltering = FALSE, cooksCutoff = FALSE,
    lfcThreshold = 0, altHypothesis = "greaterAbs"
  ) %>%
    data.frame(Gene = rownames(.), .) %>%
    dplyr::mutate(baseMean = log2(baseMean + 1)) %>%
    dplyr::rename(logFC = log2FoldChange)

  tt <- tt[, c(1, 3, 2, 4:ncol(tt))]

  # Catch corner case when all counts are zero for all columns but the gene is
  # in the dds object because the other genotype groups expressed it,
  # therefore set all columns to zero and pvalues to 1
  tt[rowSums(is.na(tt)) > 0, 2:ncol(tt)] <-
    tt[rowSums(is.na(tt)) > 0, 2:ncol(tt)] %>%
    dplyr::mutate(logFC = 0, baseMean = 0, lfcSE = 0, stat = 0, pvalue = 1, padj = 1)

  # Postfilter, requiring that at least one group of the contrast expressed the gene
  # with at least 100*<num> percent of its cells, if not then set stats to 1
  num <- 10
  fr <- pseudobulk_percent_expressed[tt$Gene, x[2:3]]
  tt <- cbind(tt, fr)
  tt[rowSums(fr > num) == 0, ]$pvalue <- 1
  tt[rowSums(fr > num) == 0, ]$padj <- 1

  return(tt)
})

list.wt <- list()

# List of canonical markers for expected celltypes
list.wt$markers_wt_selected <-
  data.frame(
    gene_name =
      c(
        "Flt3", "Hlf", "Ly6a", "Hoxa9",
        "Elane", "S100a8", "S100a9",
        "Ly86", "Csf1r", "Irf8",
        "Lmo4", "Prss34", "Pbx1",
        "Fli1", "Pf4", "Gata2", "Gfi1b",
        "Car1", "Cpox"
      )
  ) %>%
  dplyr::left_join(x = ., y = data.frame(rowData(sce)), by = "gene_name") %>%
  dplyr::mutate(gene = paste(gene_id, gene_name, sep = "_")) %>%
  dplyr::pull(gene)

markers_wt <- pseudobulk_markers[grep("WT_", names(pseudobulk_markers))]

list.wt$markers_wt_unique <-
  rld[unique(unlist(markers_wt)), grep("WT", colnames(rld))] %>%
  reshape2::melt() %>%
  mutate(Var2 = gsub("_rep.*", "", Var2)) %>%
  group_by(Var1, Var2) %>%
  summarize(mean = mean(value)) %>%
  tidyr::pivot_wider(names_from = "Var2", values_from = "mean") %>%
  rename(Gene = Var1)

# this summarizes in which cluster the given gene is most highly-expressed (average of the pseudobulks)
list.wt$markers_wt_unique_max <-
  list.wt$markers_wt_unique %>%
  reshape2::melt() %>%
  magrittr::set_colnames(c("gene", "cluster", "expression")) %>%
  dplyr::group_by(gene) %>%
  dplyr::filter(expression == max(expression))

hm_text <- 5

# Z score for each marker based on the rlog counts, trimmed to 1/99th percentile
# to avoid large color scale:
list.wt$markers_wt_z <- scale_by_quantile(
  rowScale(rld[as.character(list.wt$markers_wt_unique_max$gene), grep("WT", colnames(rld))]),
  .01, .99
)

# All this codes decides whether the genes are displayed on the left or right
# of the heatmap
list.wt$on_left_wt <-
  list.wt$markers_wt_selected[list.wt$markers_wt_selected %in%
    list.wt$markers_wt_unique_max[grep("C1|C5|C7", list.wt$markers_wt_unique_max$cluster), ]$gene]

list.wt$on_left_wt <-
  rowAnnotation(foo = anno_mark(
    at = match(list.wt$on_left_wt, rownames(list.wt$markers_wt_z)),
    labels = gsub(".*_", "", list.wt$on_left_wt), side = "left",
    labels_gp = gpar(fontsize = gg2gp(hm_text * .75))
  ))

list.wt$on_right_wt <-
  list.wt$markers_wt_selected[list.wt$markers_wt_selected %in%
    list.wt$markers_wt_unique_max[grep(
      "C3|C6|C8",
      list.wt$markers_wt_unique_max$cluster
    ), ]$gene]
list.wt$on_right_wt <-
  rowAnnotation(foo = anno_mark(
    at = match(list.wt$on_right_wt, rownames(list.wt$markers_wt_z)),
    labels = gsub(".*_", "", list.wt$on_right_wt), side = "right",
    labels_gp = gpar(fontsize = gg2gp(hm_text * .75))
  ))

list.wt$wt.lab <- sapply(strsplit(colnames(list.wt$markers_wt_z), split = "_"), function(x) x[2])

list.wt$wt.colorramp <-
  circlize::colorRamp2(
    c(floor(min(list.wt$markers_wt_z)), 0, ceiling(max(list.wt$markers_wt_z))),
    c("darkblue", "white", "darkred")
  )

ssplit <- stringr::str_split_fixed(colnames(list.wt$markers_wt_z), "_", 3)
list.wt$wt_colors_topannot <- list.ggplot$colorblind_cols[as.numeric(gsub(
  "C",
  "",
  unique(ssplit[, 2])
))]

ht_opt$message <- FALSE

# Now finally the heatmap
hm_wt_bulk <- Heatmap(
  matrix = list.wt$markers_wt_z,
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  name = "relative expression",
  show_column_names = FALSE,
  show_row_names = FALSE,
  col = list.wt$wt.colorramp,
  column_split = list.wt$wt.lab,
  column_title = NULL,
  left_annotation = list.wt$on_left_wt,
  right_annotation = list.wt$on_right_wt,
  top_annotation = HeatmapAnnotation(foo = anno_block( # gp=gpar(fill=list.wt$wt_colors_topannot),
    labels = unique(list.wt$wt.lab),
    labels_gp = gpar(col = "black", fontsize = gg2gp(hm_text))
  )),
  heatmap_legend_param = list(
    legend_direction = "horizontal",
    legend_width = unit(4, "cm"),
    legend_position = "bottom",
    labels_gp = gpar(fontsize = gg2gp(hm_text)),
    color_bar = "continuous",
    title_position = "topcenter",
    title_gp = gpar(fontsize = gg2gp(hm_text))
  )
)


pdf(NULL)
Figure_1B <- draw(hm_wt_bulk, heatmap_legend_side = "bottom")
invisible(dev.off())
Figure_1B

pu1_levels_singlecell <-
  data.frame(
    logcounts = logcounts(sce[grep("Spi1$", rownames(sce), value = TRUE), ])[1, ],
    cluster = sce$cluster,
    genotype = sce$genotype
  ) %>%
  dplyr::mutate(cg = paste(genotype, cluster, sep = "_"))

Figure_1C <-
  pu1_levels_singlecell %>%
  dplyr::mutate(logcounts = dplyr::case_when(
    logcounts > quantile(logcounts, .995) ~ quantile(logcounts, .995),
    TRUE ~ logcounts
  )) %>%
  ggplot(aes(x = genotype, y = logcounts, fill = genotype)) +
  geom_boxplot(outlier.shape = NA) +
  facet_wrap(~cluster, nrow = 1) +
  scale_fill_manual(name = "", values = list.ggplot$genotype_colors) +
  geom_vline(data = tibble(f = 2, x = c(-1, 1) * Inf), aes(xintercept = x), col = "black") +
  xlab("") +
  ylab("PU.1 expression [log2]") +
  guides(x = guide_axis(angle = 90)) +
  theme(legend.position = "bottom", legend.justification = "left", panel.spacing = unit(0, "lines")) +
  gg.noX +
  coord_cartesian()

Figure_1C

# Percent residual PU.1 expression in URE relative to WT
Appendix_Figure_S1H <-
  pu1_levels_singlecell %>%
  mutate(counts = 2^logcounts - 1) %>% # logcounts back to normal with prior of 1
  group_by(cg) %>%
  summarize(values = mean(counts)) %>%
  dplyr::mutate(
    cluster = gsub(".*_", "", cg),
    genotype = gsub("_.*", "", cg)
  ) %>%
  group_by(cluster) %>%
  summarise(percent_PU1_left_in_URE = round(100 * values[genotype == "URE"] / values[genotype == "WT"], 2)) %>%
  knitr::kable(.)

Appendix_Figure_S1H

Appendix_Figure_S4B <-
  data.frame(
    logcounts = logcounts(sce[grep("Flt3$", rownames(sce), value = TRUE), ])[1, ],
    cluster = sce$cluster,
    genotype = sce$genotype
  ) %>%
  dplyr::mutate(cg = paste(genotype, cluster, sep = "_")) %>%
  dplyr::mutate(logcounts = dplyr::case_when(
    logcounts > quantile(logcounts, .995) ~ quantile(logcounts, .995),
    TRUE ~ logcounts
  )) %>%
  ggplot(aes(x = genotype, y = logcounts, fill = genotype)) +
  geom_boxplot(outlier.shape = NA) +
  facet_wrap(~cluster, nrow = 1) +
  scale_fill_manual(name = "", values = list.ggplot$genotype_colors) +
  geom_vline(data = tibble(f = 2, x = c(-1, 1) * Inf), aes(xintercept = x), col = "black") +
  xlab("") +
  ylab("Flt3 expression [log2]") +
  guides(x = guide_axis(angle = 90)) +
  theme(legend.position = "bottom", legend.justification = "left", panel.spacing = unit(0, "lines")) +
  gg.noX +
  coord_cartesian()

Appendix_Figure_S4B

normalized_cell_counts <-
  as.data.frame(table(sce$genotype, sce$cluster)) %>%
  magrittr::set_colnames(c("genotype", "cluster", "cellnumber")) %>%
  dplyr::group_by(genotype) %>%
  dplyr::mutate(cellnumber_normalized = (10000 * cellnumber / sum(cellnumber))) %>%
  dplyr::mutate(cellnumber_normalized = ceiling(cellnumber_normalized)) %>%
  as.data.frame()

# Cell numbers normalized to 10k cells per sample
Figure_1D <-
  normalized_cell_counts %>%
  ggplot(aes(x = cluster, y = cellnumber, fill = genotype)) +
  geom_bar(stat = "identity", position = "dodge") +
  ylab("norm. cell counts") +
  scale_fill_manual(values = list.ggplot$genotype_colors) +
  xlab(element_blank()) +
  theme(legend.position = "top", legend.justification = "left", legend.title = element_blank())

Figure_1D

singler_ref_genes <- list(C3 = pseudobulk_markers$WT_C3, C5 = pseudobulk_markers$WT_C5)
singler_ref_dds <- dds_pseudobulk[, dds_pseudobulk$cluster %in% c("C3", "C5") & dds_pseudobulk$genotype == "WT"]
singler_run1 <- SingleR::SingleR(
  test = sce[, sce$cluster %in% c("C1", "C2")],
  ref = assay(singler_ref_dds, "counts"),
  labels = droplevels(singler_ref_dds$cluster),
  genes = singler_ref_genes
)

# Show general shift towards a neutrophil profile
Figure_1E <-
  data.frame(cell = rownames(singler_run1), singler_run1$scores) %>%
  left_join(x = ., y = data.frame(colData(sce)), by = "cell") %>%
  filter(cluster %in% c("C1", "C2")) %>%
  mutate(cluster = gsub("C2", "C2 cells", gsub("C1", "C1 cells", cluster))) %>%
  ggplot(aes(x = C3, y = C5, color = genotype)) +
  geom_density_2d() +
  facet_wrap(~cluster, nrow = 1, scales = "free") +
  geom_abline(size = .25, lty = 2) +
  xlab("neutrophil lineage score") +
  ylab("monocyte lineage score") +
  scale_color_manual(values = list.ggplot$genotype_colors, name = "") +
  theme(legend.position = "top", legend.justification = "left")

Figure_1E

# Below here is all figure Figure 1F --
# Trajectory analysis -- order cells in pseudotime
s.sling <- sce[, sce$cluster %in% c("C1", "C2", "C3", "C4", "C5")]

reducedDim(s.sling, "corrected_slingshot") <- reducedDim(s.sling, "corrected")[, 1:49]

s.sling <- slingshot::slingshot(
  data = s.sling, clusterLabels = droplevels(s.sling$cluster),
  reducedDim = "corrected_slingshot", approx_points = 100,
  start.clus = "C1", end.clus = c("C4", "C5")
)

which_trajectory <-
  data.frame(colData(s.sling)) %>%
  dplyr::select(cluster, genotype, contains("slingPseudotime_")) %>%
  reshape2::melt(
    measure.vars = grep("slingPseudotime_", colnames(.), value = TRUE),
    variable.name = "trajectory", value.name = "pseudotime"
  ) %>%
  group_by(trajectory, cluster) %>%
  summarize(mean = quantile(pseudotime, .75, na.rm = TRUE)) %>%
  group_by(trajectory) %>%
  slice_max(order_by = mean, n = 1) %>%
  mutate(cluster = gsub("C6", "basophil", gsub("C5", "monocyte", gsub("C4", "neutrophil", cluster))))

which_trajectory

# Plot monocyte and neutrophil progenitor signature along the C1 trajectory
signatures_to_score <- markers_wt[c("WT_C3", "WT_C5")] %>% setNames(c("neutrophil", "monocyte"))

# Lots of custom code to plot the lineage scores along trajectory because there is unequal numbers of cell,
# so we do binning of adjacent cells to have the same dimension for the x-axis in neutrophil and monocyte progenitors
Figure_1F <-
  lapply(names(signatures_to_score), function(x) {
    current_signature <- signatures_to_score[[x]]
    subset_to <- NULL

    # We subset to C1-C3 first so the loess (below) is not skewed at the C1 border
    if (x %in% c("monocyte")) {
      subset_to <- c("C1", "C2", "C5")
      use_trajectory <- as.character(which_trajectory[which_trajectory$cluster == "monocyte", "trajectory", drop = TRUE])
    }

    if (x %in% c("neutrophil")) {
      subset_to <- c("C1", "C2", "C3")
      use_trajectory <- as.character(which_trajectory[which_trajectory$cluster == "neutrophil", "trajectory", drop = TRUE])
    }

    subset_to <- if (is.null(subset_to)) unique(sce$cluster) else subset_to

    current_signature_found <- intersect(current_signature, rownames(s.sling))
    sx <- s.sling[current_signature_found, s.sling$cluster %in% subset_to]

    # The score is the mean of the mean-subtracted logcounts for the signature genes
    d <- assay(sx, "logcounts")
    d_norm <- d - rowMeans(d)

    # Now calculate the median per cell
    d_score <- as.data.frame(apply(d_norm, 2, mean)) %>%
      rownames_to_column("cell") %>%
      setNames(c("cell", "score"))
    d_cold <- data.frame(colData(sx)) %>% dplyr::select("cell", "cluster", "genotype", all_of(use_trajectory))
    colnames(d_cold)[colnames(d_cold) == all_of(use_trajectory)] <- "pseudotime"
    d_score <- d_score %>% left_join(x = ., y = d_cold, by = "cell")

    # Bin each cluster into 10 chunks and take the median of the per-cell score. This compensates zeros and also
    # deals with uneven cell numbers between genotypes, so in the end we can show equally-sized clusters.
    n_chunks <- 100
    d_data <-
      d_score %>%
      arrange(pseudotime) %>%
      dplyr::group_by(cluster, genotype) %>%
      dplyr::mutate(chunk = rep(1:n_chunks, each = n() / n_chunks, length.out = n())) %>%
      dplyr::group_by(cluster, chunk, genotype) %>%
      dplyr::summarise(score = median(score)) %>%
      group_by(genotype) %>%
      arrange(cluster, genotype) %>%
      mutate(idx = 1:n()) %>%
      group_by(genotype) %>%
      mutate(
        signature = x,
        y = predict(loess(score ~ idx, span = .25, family = "symmetric"))
      ) %>%
      filter(cluster %in% c("C1"))

    ymax <- max(d_data$score)
    ymin <- min(d_data$score)
    yrange <- seq(ymin, ymax, length.out = 3)

    p <-
      d_data %>%
      ggplot(aes(x = idx, y = score, color = genotype)) +
      geom_point(size = .2) +
      geom_line(aes(y = y), show.legend = FALSE) +
      guides(color = guide_legend(override.aes = list(size = 3))) +
      xlab("relative pseudotime") +
      ylab("relative expression score") +
      scale_x_continuous(breaks = c(1, n_chunks), labels = c("low", "high")) +
      scale_y_continuous(breaks = yrange, labels = c(-1, 0, 1)) +
      scale_color_manual(values = list.ggplot$genotype_colors, name = "") +
      facet_wrap(~signature)

    if (x == "monocyte") {

      # For aesthetics we remove the yaxis of the monocyte plot.
      # It's a relative score anyway and only the difference per signature between genotype counts so it's ok I think
      p <- p + theme(
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank()
      )
    }

    return(p)
  }) %>% patchwork::wrap_plots() + plot_layout(guides = "collect") & theme(legend.position = "top", legend.justification = "left")